In the art of image processing, it will often be the case that image information of interest is intermixed with, or even masked by other image information which is not of interest. Thus an objective in such cases will be a separation of the image information of interest from the information which is not of interest. An important image processing application in which this objective may come into play is found in the field of non-invasive imaging of anatomical structures and physiological processes. An exemplary such application is Magnetic Resonance Imaging ("MRI"), and particularly the MRI subclass known as functional Magnetic Resonance Imaging ("fMRI") which has been shown to be particularly useful in the identification of parts of the brain associated with specific cognitive processes. Hereafter fMRI will be used both to illustrate the problems solved by the invention and as a preferred embodiment of the invention. It will be understood however that the invention is broadly applicable to the processing of image data generally, particularly to such images generated in a medical or clinical environment.
As will be known to those skilled in the art, functional Magnetic Resonance Imaging of the brain holds great promise as a tool to elucidate the functioning of the human brain [See, for example, applications of fMRI described in S. Ogawa et al., "Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging", Proc. Natl. Acad. Sci. USA, 89 5951 (1992) and K. K. Kwong et al., "Dynamic magnetic resonance imaging of human brain activity 5 during primary sensory stimulation", Proc. Natl. Acad. Sci. USA, 89 5675 (1992)]. Changes in the oxygen content in cerebral blood causes small but detectable changes in an MR image. Since the oxygen content of the blood is known to be locally dependent on brain activity, detection of such changes in oxygen content provides a particularly reliable indicator of moment-to-moment brain function. There are, however, problems that limit the full utility of this technique. A major problem is that signal levels for changes in the image related to function are fairly small, and could well be lost among unwanted sources of image fluctuation. Such undesirable variations in the image include incoherent noise, approximately periodic fluctuations arising from physiological sources--e.g., cardiac and respiratory cycles, and motion of the experimental subject. Previous data analysis techniques have generally relied on statistical tests of significance to extract spatial maps of brain regions showing correlations with a stimulus timecourse. [See for example, P. A. Bandettini et al., "Processing Strategies for Time-Course Data Sets in Functional MRI of the Human Brain", Magnetic Resonance in Medicine, 30 161 (1993)].
The conventional data analysis approaches generally have three drawbacks:
(i) they do not take into account the complete structure of the signal and the noise, thus preventing optimal detection of the signal; PA1 (ii) such methods essentially produce maps of static brain regions, and it is difficult to assess the full spatio-temporal nature of the image signal in this way; and PA1 (iii) an analysis methodology based on looking for changes correlated to the stimulus time course prevents detection of events that do not appear in synchrony with the stimulus.
As is known, techniques have been suggested in attempts to overcome the above drawbacks. To alleviate (ii), maps have been produced showing correlations with several shifted versions of the stimulus time course [See, E. A. DeYoe et al., "Functional Magnetic Resonance Imaging of the Human Brain", J. Neuroscience Methods, 54 171 (1994)]. To avoid drawback (iii), the use of Principal Component Analysis (or equivalently Singular Value Decomposition) has been proposed to extract coherent changes in the signal that are not necessarily locked to the stimulus time course. [See J. R. Baker et al., "Statistical Assessment of Functional MRI Signal Change", 2 626, Proceedings of the Society of Magnetic Resonance, Second Meeting Aug. 6-12, 1994, San Francisco, Calif.] However, such proposals have not led to a comprehensive solution to the full problem.